Computer Science Topics List for Thesis, Research, and Project
Computer Science is the most popular and thriving field of study these days due to a large number of career opportunities in this field of study. Nowadays, computer science finds its application in almost every field from healthcare to manufacturing. The number of job opportunities in this field is going to increase in the coming future since we are moving towards an era of digitization. For this digitization, there is a need for highly skilled computer science engineers. There are various computer science topics and fields in which you can do mastery.
In order to excel in any field of computer science, theoretical as well as practical skills are mandatory. Besides this, you should also have proficiency in programming. You should have the ability to handle all the technical tasks assigned to you. Talking about academics, there are various fields in which you can do the research and write an M.Tech thesis. Following is the list of latest thesis topics for computer science students:
- Image Processing
- Mobile Cloud Computing
- Natural Language Processing
- Wireless Ad-hoc Network
- Data Mining
- Computer Vision
- Big Data
- Internet of Things(IoT)
- Machine Learning
- Wireless Body Area Network(WBAN)
Image Processing is a technique for performing operations on digital images in order to alter their features and enhance their quality. Through image processing, we can also extract valuable statistics from the images. It is one of the rapidly growing fields these days and a hot topic for research and thesis in computer science. Image Processing is further divided into two types:
- Analog Image Processing
- Digital Image Processing
Here we are talking about Digital Image Processing or DIP
Applications of Image Processing
It has a number of useful applications. Following are the main application areas of image processing:
- Image Restoration and Enhancement
- UV Imaging
- X-Ray Imaging
- Pattern Recognition
- Color Vision
- Video Processing
- Transmission and encoding
Further Types of Image Processing
Along with analog and digital image processing, there are some other types of image processing as well which are:
Satellite Image Processing – Satellite image processing helps in fetching high-resolution images of the earth and the outer space through hi-tech cameras.
Medical Image Processing – Medical Image Processing or Medical Imaging finds its application in various medical research areas. Through medical imaging, internal areas of the body can be visually represented to study functions of various internal organs of the body.
Mobile Cloud Computing
Mobile Cloud Computing or MCC is a technology which is a combination of mobile computing, cloud computing, and wireless networks to provide computational resources to mobile users, cloud providers, and network operators. In other words, we can say that mobile cloud computing delivers applications to mobile devices using the concept of cloud computing. It offers tremendous business opportunities for mobile network operators as well as cloud service providers. It is also a good topic for an M.Tech thesis in computer science.
Features of Mobile Cloud Computing
Following are the key features of mobile cloud computing:
- It supports a variety of devices as well as development approaches.
- Services delivered on API architecture.
- Improved reliability as there is a backup of information stored in the cloud.
- Resources of the mobile application are shared thereby facilitating the quick development.
- Data can be integrated from a variety of resources.
Types of Cloud-based Resources
There are mainly four types of cloud-based resources in mobile cloud computing which are:
- Distant immobile clouds
- Proximate immobile computing entities
- Proximate mobile computing entities
- Hybrid computing entity
There is a significant research and development going on in the field of Mobile Cloud Computing
Natural Language Processing
Natural Language Processing or NLP is an application area of Artificial Intelligence through which computers can understand and manipulate human language. It is also an important area for the thesis in computer science. It has the following two main components:
Natural Language Understanding – Mapping the natural language into useful representations
Natural Language Generation – Generating meaningful sentences from the representations
Steps of Natural Language Processing
Following are the five steps in Natural Language Processing:
Lexical Analysis – In this step, the whole content is divided into paragraphs, sentences, and words. The structure of the words is identified and analyzed here.
Syntactic Analysis – In this step, the grammar is analyzed so as to ensure that the words arranged in such a manner to make a logical sentence.
Semantic Analysis – Meaning of the text is checked by mapping syntactic structures and objects in the task domain.
Discourse Integration – The meaning of the sentence before and after a particular sentence is checked such that there is a relationship between them.
Pragmatic Analysis – It requires real-world knowledge to interpret what is said.
Wireless Ad-hoc Network
Wireless Ad-hoc Network or WANET is a type of wireless network that does not use pre-existing infrastructure for communication. In this network, routing is initiated by a node that transfers the data packet to other nodes by following data routing algorithms. The nodes in the network communicate with each other directly without any central access point like a router.
Advantages of Wireless Ad-hoc Network
Following are the main advantages of Wireless Ad-hoc Network:
- Less cost of infrastructure
- Use unauthorized frequency spectrum
- Data is quickly distributed across the network
- The performance of the network is high
Data Mining is a process of extracting information and discovering patterns from the large data-sets. It is another trending technology these days and an important area of research. The information extracted from data-sets can be used for future predictions.
Steps of Data Mining process
The process of data mining revolves around the following steps:
- The data is extracted, transformed, and loaded into a data warehouse
- The data is stored and managed in a multidimensional database
- Business analysts get access to data using application software
- The analyzed data is represented in the form of graphs
The process of Data Mining
Following are the phases of data mining process:
- Problem Definition
- Data Gathering and Preparation
- Model Building and Evaluation
- Knowledge Deployment
Applications of Data Mining
The major applications of data mining are:
- Corporate Analysis
- Fraud Detection
- Market Growth Analysis
- Risk Management
- Intrusion Detection
- Financial Banking
Computer Vision is another important area of computer science. It is a sub-field of artificial intelligence that aims to provide human vision and perception to computers. For computer vision, real-world data is taken into consideration in order to make decisions. There are certain algorithms developed for the process of computer vision. It involves three main processes which are:
- Image Acquisition
- Image Processing
- Image Analysis and interpretation
Computer Vision Applications
The main applications of computer vision are:
- Automatic Inspection
- Event Detection
- Virtual Reality
- Object Modeling
- Autonomous cars
- Visual Surveillance
- Image Restoration
Computer Vision is among the top trending computer science topics. Signal processing is another field related to computer vision which deals with analysis and modification of signals.
Big Data refers to the collection of a large amount of data generated through different sources and which may be structured or unstructured. This data is complex to manage and certain advanced techniques like predictive analytics are used in order to extract information from this data. There are various application areas of big data including government, healthcare, business analysis, e-commerce, meteorology etc. The characteristics of Big Data is defined with three Vs namely:
Velocity: The pace at which the data is produced.
Variety: Different sources from where the data is generated.
Volume: The quantity of data being produced.
Along with these, there are two other variables also to define big data:
Variability: Inconsistency in data while it is being generated.
Veracity: Quality of data.
Types of Big Data
Big Data can be categorized into three forms which are:
- Structured Data – A set of data having a fixed format
- Unstructured Data – Dataset which does not have a fixed format
- Semi-Structured Data – It is a combination of both structured and unstructured data
Advantages of Big Data
Big Data is the latest rending technology that provides the following advantages:
- Businesses can improve their business strategies by taking insights from Big Data.
- With improved tactics, businesses can provide better customer services.
- With big data analytics, one can make better decisions for future.
Internet of Things(IoT)
Internet of Things or IoT is a technology through which a number of physical devices are connected to each other through the internet. The devices are equipped with sensors and actuators to act according to their surrounding environment. This technology is constantly evolving. There are various applications of the Internet of Things (IoT) which includes home automation, smart cars, environment monitoring, smart traffic control, etc.
List of IoT Devices
Following is the list of IoT devices:
- Google Home Mini
- Nest Thermostat
- Smart Lock
- Nest Cam
- Smart Air Quality Monitor
Machine Learning is an application of Artificial Intelligence that provides computers and systems the capability to learn automatically from the previous data without being specifically programmed. It is currently the hot field both for research and thesis. There are algorithms designed for machine learning process such that the systems can identify patterns from data-sets and make decisions accordingly. Machine Learning algorithms are categorized into following three types:
Supervised Learning – In supervised learning, we have an input variable x and an output variable y such that the output is mapped to the input through a mapping function such that y = F(X). Supervised learning can be further classified into:
- Classification: A problem in which an output variable is a category.
- Regression: Output variable is a real value.
Unsupervised Learning – In unsupervised learning, we only have input data and no output data. The main goal of this type of algorithm is to explore the data and identify the underlying structure. It can be further divided into:
- Clustering: In this type of problem, data with similar patterns are grouped together.
- Association: In this problem, rules are discovered that describes the data.
Reinforcement Learning – This type of learning method interacts with its environment and is mainly used for gaming, robotics, and navigation.
Applications of Machine Learning
Machine Learning is a very good choice for the thesis topic in computer science. There are various applications of machine learning some of which are:
- Virtual Personal Assistant
- Video Surveillance
- Online Customer Support
- Online Fraud Detection
- Image Recognition
- Speech Recognition
- Medical Diagnosis
- Statistical Arbitrage
Wireless Body Area Network(WBAN)
Wireless Body Area Network or WBAN is a wireless network in which some computing devices are embedded inside the human body to monitor the health of a person and his day-to-day activities. The idea of WBAN originated from the concept of wireless personal area network(WPAN). It has paved the way for the new era of medical diagnosis.
There are two major advantages of WBAN:
- It allows mobility of the patient as the monitoring devices are portable.
- The health of the patient can be monitored from a distant location.
The sensors implanted inside the human body captures physiological changes including heart rate, body temperature, blood pressure.
Components of Body Area Network
A normal body area network consists of the following components:
Challenges of Wireless Body Area Network
There are certain challenges of this technology which needs to be tackled:
- The data generated through this network should be of high quality in order to make appropriate decisions concerning patient’s health.
- As a large volume of data is generated, it should be managed with care.
- Transmission of data should be secure and accurate.
- The sensors used in the network should be light-weight, power efficient, and easy to use.
- The performance of the network should be consistent such that there is no loss of data when the network is switched off.
This was the list of latest topics in computer science for thesis and research purpose. There are various other computer science topics but these are the current hot topics in this field. Students who need any kind of thesis writing help in any of these topics or some other topic of computer science can contact us. Writemythesis provides complete assistance to research students and those writing an M.Tech thesis in computer science.